Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays

Abstract

This study first explored the effect of shading on the output characteristics of modules in a photovoltaic module array. Next, a modified particle swarm optimization (PSO) method was employed to track the maximum power point of the multiple-peak characteristic curve of the array. Through the optimization method, the weighting value and cognition learning factor decreased with an increasing number of iterations, whereas the social learning factor increased, thereby enhancing the tracking capability of a maximum power point tracker. In addition, the weighting value was slightly modified on the basis of the changes in the slope and power of the characteristic curve to increase the tracking speed and stability of the tracker. Finally, a PIC18F8720 microcontroller was coordinated with peripheral hardware circuits to realize the proposed PSO method, which was then adopted to track the maximum power point of the power-voltage (P-V) output characteristic curve of the photovoltaic module array under shading. Subsequently, tests were conducted to verify that the modified PSO method exhibited favorable tracking speed and accuracy.

Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

“…The existing improved PSO [21] targets the weight value W of the traditional PSO. The modification method in Equation (3) enables particles to use larger moves in the early phase of iteration to perform the search, preventing it from failing to skip the local best solution due to overly small moves.…”

“…This modification method features the advantage of requiring fewer iterations compared to the traditional PSO and accurate tracking. However, it also has the disadvantage of failing to approximate the actual MPP in the early phase of iteration, resulting in a substantial increase in the number of iterations required [21].…”

“…In terms of learning factors, since cognition learning factor C 1 uses Equation (4) to decrease the number of iterations, it means that, as the number of iterations increases, the best location value of its own particles as reference decreases; C 2 uses Equation (5) to increase the number of iterations, which means that, as the number of iterations increases, the global best location value of the particle is emphasized [21].…”

“…where the definition of the weight value W parameters is the same as in the PSO proposed in Reference [21]. (6) where the definition of the weight value W parameters is the same as in the PSO proposed in Reference [21].…”

“…where the definition of the weight value W parameters is the same as in the PSO proposed in Reference [21]. (6) where the definition of the weight value W parameters is the same as in the PSO proposed in Reference [21]. In terms of learning factors, cognition learning factor (C1) is regarded as each individual bee, which, combined with ABC [22], is modified using Equation (7).…”